High Kellgren-Lawrence Grade and Bone Marrow Lesions Predict Worsening Rates of Radiographic Joint Space Narrowing; The SEKOIA Study
Bibliographic record
Abstract
OBJECTIVE: Determinants of radiographic progression in osteoarthritis (OA) are poorly understood. We investigated which features on baseline magnetic resonance imaging (MRI) acted as predictors of change in joint space width (JSW). METHODS: A total of 559 men and women over the age of 50 years with clinical knee OA [Kellgren-Lawrence (KL) grade 2-3] were recruited to the placebo arm of the SEKOIA study (98 centers; 18 countries). Minimal tibiofemoral joint space and KL grade on plain radiograph of the knee were assessed at baseline and at yearly followup up to 3 years. In a subset, serial knee MRI examinations were performed. Individuals with a bone marrow lesion (BML) ≥ grade 2 at the tibiofemoral joint at baseline were classified as BML-positive. Relationships between change in JSW and risk factors were assessed using linear regression. RESULTS: The mean age of study participants was 62.8 (SD 7.5) years and 73% were female; 38.6% had BML. Mean baseline JSW was 3.65 mm. This reduced by 0.18 (0.30) mm/year in men and 0.13 (0.23) mm/year in women. Those with BML had a significantly higher rate of annualized change in JSW; this relationship remained robust after adjustment for age, sex, and baseline KL grade [β = -0.10 (95% CI -0.18, -0.02) mm/yr]. Age, sex, baseline KL grade, and other MRI findings did not influence the rate of change in JSW. CONCLUSION: The rate of change in JSW was similar in men and women. BML on knee MRI predicted the rate of radiographic change in JSW. This relationship was independent of age, sex, and baseline KL grade.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".